KEYWORDS: Phase shifts, Holograms, Holography, Background noise, Digital holography, Deep learning, Phase unwrapping, Optical simulations, Network architectures, Signal to noise ratio
In-line holography has been widely used in various fields because of its advantages such as simple optical path, low requirement of light source coherence and low utilization of the camera spatial bandwidth product, but it is difficult for in-line holography to restore object information from a single in-line hologram. The traditional phase-shifting algorithm requires at least three phase-shifting holograms, moreover the error caused by the intermediate phase-shifting may be accumulated and amplified. In recent years, deep learning has been widely used in the optical field due to its advantages in data analysis. Deep learning has provided new solutions for in-line holographic reconstruction, which can solve the problems that are difficult to avoid in traditional methods. In this paper, a method for generating phase-shifting holograms based on a modified Y-4net is proposed to reduce the experimental workload in data collection and the noise in phase-shifting image, which is referred to as Ps-4net. The proposed Ps-4net can generate four virtual phase-shifting fringe patterns from a single frame hologram and calculate the phase from virtual phase-shifting holograms. Simulation and experimental results show that the Ps-4net can effectively reduce the workload of data collection and the phase-shifting hologram is generated and the noise is removed at the same time.
Quantitative phase information which can reflect the internal structure and refractive index distribution of the object is able to be obtained by diffractive and interferometry techniques. However, the phase resolution achieved by the diffraction method is lower than that of interferometry method; while the setup for interferometry method is more complex. To obtain high-resolution phase images without reference beam path, we propose an end-to-end DL based super resolved quantitative phase imaging method (AF-SRQPI) based on generative adversarial network (GAN) to transform low-resolution amplitude images into super-resolved phase images. Meanwhile, considering the inevitable out-focusing during the long hours of observing, autofocusing function is also included by the network. In the training process, out-of-focus low-resolution amplitude images are used as the inputs and corresponding super-resolved phase images obtained by structured illumination digital holographic microscopy (SI-DHM) are used as the ground truth labels. The well-trained network can reconstruct the high-resolution phase image at high speed (20fps) from a single-shot out-of-focus amplitude image. Comparing with other DL-based reconstruction schemes, the proposed method can perform autofocusing and superresolution phase imaging simultaneously. The simulation results verify that the high-resolution quantitative phase images of different biological samples can be reconstructed by using AF-SRQPI .
Perfect optical vortices (POVs), consists of a single bright ring structure, has been widely studied owing to its radius independent of orbital angular momentum (OAM). However, most of the existing works about POVs are limited to single ring structure. Flexible shaping of intensity distribution of POVs is vital for multiple applications. In this paper, we propose a method generate phase tunable multi-ring perfect optical vortices (MR-POVs) where each ring size is independent of its OAM. The scheme is based on the radical discontinuous spiral phase plate (RD-SPP) which introduces controllable phase jumps along radial direction. It is experimentally demonstrated that the vortex nature of the MR-POVs through an interferometric method, showing that each ring of MR-POVs possesses same topological charge value (magnitude and sign), and the intensity ratio between each ring can be freely regulated by adjusting phase distribution, which could offer more flexible optical gradient force for guiding and transporting particles. In addition, simulation and experimental results show that the integer and fractional MR-POV can generated by the independent regulation of angular and radial factors. This work expands our understanding of multi-ring POV and may provide a new idea for optical tweezers and OAM communications.
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